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© 2018 JETIR November 2018, Volume 5, Issue 11 www.jetir.org (ISSN-2349-5162)
JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 368
POWER SYSTEM STABILITY ENHANCEMENT
USING FUZZY LOGIC BASED POWER SYSTEM
STABILIZER
Bhavin D. Pargi1*
, Urvi S. Marathe2, Vishal R. Patel
3, Dr. Akshay A. Pandya
4
1Electrical Engineering Department, Birla Vishvakarma Mahavidyalaya, India
2Electrical Engineering Department, Birla Vishvakarma Mahavidyalaya, India
3Electrical Engineering Department, Birla Vishvakarma Mahavidyalaya, India
4Electrical Engineering Department, Birla Vishvakarma Mahavidyalaya, India
Abstract
“Power system is dynamic in nature and it is constantly subjected to disturbances and
unpredictable faults. It's main objective that these disturbances and faults do not lead the
system to unstable condition. Power system stabilizers (PSS) are used to improve the
damping during low frequency oscillation and other faults under these disturbances. PSS are
designed by conventional and non-conventional controllers. Conventional controller’s uses
phase lead compensation techniques, but they cannot provide best performance in all loading
condition. To cover wide range of conditions non-conventional controllers used such as
Fuzzy Logic controller. This paper presents the study about PSS using Fuzzy Logic to
enhance stability of single machine infinite bus system. The input of synchronous machine
are Speed deviation and acceleration. The supplementary voltage signals are taken from
fuzzy logic controller are given to excitation system of the synchronous machine. Results
presented in this paper shows that fuzzy logic based PSS design gives much more better
performance than conventional PSS.
Keyword: single machine infinite bus system, Power System Stabilizer (PSS), Fuzzy Logic
Controller (FLC)
INTRODUCTION
"Power systems are dynamic in nature. they subjected to low frequency disturbance due to sudden
load change and etc. that might cause loss of synchronism or an sometime it’s the reason for failure of
whole system. The oscillations, which are in frequency range of 0.2 to 0.3 Hz, might be generate by
the disturbance in system or sometime build up spontaneously. These low frequency oscillations are
generator rotor angle oscillations which limit the power capability of the system and sometime they
can stop the entire system. For this reason, power system stabilizers are used to generate the
supplementary control signal to damp out these low frequency oscillation (LFO). Now a days mostly
conventional power system stabilizers are used to overcome this type of faults. The CPSS can be
designed using classical methods such as Eigen value, root locus, and phase compensation etc. In this
paper CPSS use phase compensation where the gain setting is already fixed for some situations or
some specific operations but the constant changing nature of power system makes more complicated
task. So it is more difficult to design a PSS that could give good performance in all operations. To
solve this problem fuzzy logic based technique suggested. Using fuzzy logic based tech. mathematical
model of system are not necessary, easy to improve and computationally efficient. The fuzzy logic
based PSS are designed on single machine infinite bus system and compare the performance between
© 2018 JETIR November 2018, Volume 5, Issue 11 www.jetir.org (ISSN-2349-5162)
JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 369
the CPSS and FPSS. Result shows that the better performance of fuzzy logic based power system
stabilizer(FLPSS) in comparison to the conventional power system stabilizer(CPSS).”
SYSCHRONOUS MACHINE MODEL
“The synchronous machine connected to a large system through transmission lines. Fig 1 show the
configuration of SMIB. Synchronous machine connected to infinite line can be represented as the
thevenin’s equivalent circuit where Et is terminal voltage and Eb is bus voltage.”
CLASSICAL SYSTEM MODEL:
“The generator is represented as the voltage E' behind Xd' as shown in Fig. 1.2. The magnitude of
E' is assumed to remain constant at the pre-disturbance value. Let d be the angle by which E' leads
the infinite bus voltage EB. The d changes with rotor oscillation. The line current is expressed as”
0 '0 ( cos sin )B B
t
t t
E E E E jI
jX jX
Figure 1: General Configuration of System
Figure 2: Equivalent System
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JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 370
sin '( ' cos )B B
t t
EE E E ES P jQ j
X X
With stator resistance neglected, the air-gap power (Pe) is equal to the terminal power (P). In per unit,
the air-gap torque is equal to the air gap power.
The above equation to describe small-signal performance is represented in schematic Fig. 1.3
From the block diagram we have
0 1( )
2s D r MK K T
s Hs
0
0
1( )
2s D M
s
K K s Ts H
Solving block diagram we get char. Equation:
Figure 3: Classical Model Of Generator
Figure 4: Block Diagram of SMIB with Classical Model
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JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 371
2 0 02 2
sDKK
s sH H
Compare with general form, the undamped natural frequency and damping ratio as
0
1
2 2
D
s
K
K H
POWER SYSTEM STABILIZER
“The basic function of power system stabilizer is to add supplementary signal to the generator rotor
oscillations by controlling its excitation using auxiliary stabilizing signals. Foe provide damping
signal the must produce a component of electrical torque in phase with rotor speed deviations. The
fig. shows the block diagram of PSS. The CPSS can use input as speed deviation of generator shaft,
accelerating power or even the terminal bus frequency. In this paper the speed deviation is used as
input and voltage signal as output of CPSS.”
FUZZY CONTROLLER
“Fuzzy logic control system are rule based system which a set of fuzzy rules present a control
decision to adjust the effect of certain system simulation With the help of effective rule base we can
Figure 5: Block Diagram of SMIB with PSS
Figure 6: Design of Fuzzy Logic Controller
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JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 372
improve our system more and control also increase. The fuzzy logic controller provide an algorithm
which can convert the control strategy into automatic control strategy. Fig. shows the fuzzy logic
controller which consists of a fuzzification interface, a knowledge base, control system, decision
making logic. And a defuzzification interface.”
SIMULATION AND RESULTS
The performance of SMIB has been studied
(1) With excitation system
(2) With conventional PSS
(3) With fuzzy logic based power system stabilizer
The data taken from:
Parameters Numerical value
P 0.9
Q 0.3
Et 1.0
F 50
Xd 1.81
Xq 1.76
Xd1 0.3
XL 0
Xe 0.65
Ra 0.003
Td01 8.0
H 3.5
0 314
DK 0
RT 0.02
TmagE 1.0
aduL 1.65
aduL 1.60
fdR 0.0006
fdL 0.153
sdK 0.8491
sqK 0.8491
sdIK 0.434
sdIK 0.434
SATA 0.031
SATB 6.93
1 0.8
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Frequency of oscillation 10
With excitation system
“The model used in the Simulink is shown in the fig. In this paper the term K is constant. And the
value of K calculated by using above parameters:”
1 2 3 40.7635, 0.8643, 0.3230, 1.4188K K K K
With conventional power system stabilizer (CPSS)
Figure 7: SMIB with AVR Only
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JETIRK006052 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 374
Figure 9: variation of angular speed, angular position and torque when PSS is applied with K5
negative
With fuzzy logic based power system stabilizer
Figure 8: SMIB with AVR and PSS
Figure 10: Simulink Model with Fuzzy Logic Based PSS Figure 11: Variation of Angular Speed, Angular Position and Torque When
PSS Is Applied With K5 Negative
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CONCLUSION
“In this paper work is carried out to damp out the oscillation of the power system using fuzzy logic
based controller on a single machine infinite bus system. FLPSS shows that superior performance
than the power system stabilizer in term of settling time and damping effect. So, we can conclude that
the performance of FLPSS is better than conventional power system stabilizer.”
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